1 About this document

A trial specifications document is intended to provide a description of an MSE framework that is sufficiently detailed to ensure reproducibility. This description includes data sources, management performance metrics, operating models, operating model dynamics, operating model conditioning, management procedures, management procedure tunings and constraints, and exceptional circumstances protocols.

Note: this is a preliminary trial specifications document for the U.S. South Atlantic dolphinfish MSE that is currently intended to elict feedback.


2 Context

2.1 Problem statement

Managers of the Atlantic dolphinfish fishery wish to identify a responsive and robust management strategy that can achieve management objectives for a wide range of plausible scnearios for the population and fishing dynamics.

Table 1. Challenges and opportunities identified by Peterson et al. 2024.

Challenges with current management approach
Highly migratory; international distribution
Data limited with no stock assessment
Short-lived and environmentally driven productivity
Static management
Regional differences in management objectives
U.S. management limited to the U.S. EEZ Atlantic Coast
Proposed solutions
Empirical (indicator-based) Management Procedure with allow for adaptive management
In years where more dolphin are available to the fishery, catch limits should increase. In years where less dolphin are available to the fishery, catch limits should decrease.
Spatial management options for equitable opportunity across states or regions
Allocation options to achieve multiple competing objectives


2.2 Aims

Peterson et al. 2024 Describe at least two broad aims of the dolphinfish MSE:

  1. predict the amount of dolphin the SAFMC will have each year

  2. maximize the usage of those fish across sectors and region


2.3 Conceptual Management Objectives

Peterson et al. 2024 identified at least 6 conceptual management objectives for the fishery operating in northern Florida and southern North Carolina.

Table 2. Conceptual management objectives identified by Peterson et al. 2024

Willing to accept more restrictions
- open to reducing trip/vessel limits
- Pro (or mixed) size limits - open to explore what is viable for the charter fishery
Area-specific management
- Mixed for sector-specific considerations (including private rec v. for hire)
Maintain accessibility / opportunity
- Fishery reliability
Ecosystem considerations
Fishery stability & maximize catches

3 Basic concepts and stock structure

3.1 Spatial definitions

Damiano et al. 2024 identify seven discrete areas for characterizing the spatial dynamics of the dolphinfish population and fishery.

Table 3. The discrete spatial strata

Code Map code Description
NED NED North coast and off NE and NC
NE VBM Montauk to Virginia Beach
NC NNC Northern North Carolina down to Wilmington
SE NCFL Wilmington down to Mid Florida
SFL FLK South Florida and the Keys
SAR SAR Off SE and SFL
CAR CAR Cancun (NW) to Trinidad and Tobago (SE)

Figure 1. Spatial strata defined in Damiano et al. 2024

The dolphinfish population spans all three of the East Coast regional management councils (New England, Mid-Atlantic and South Atlantic).

Figure 2. Management jurisdictions as described by Ammendment 10 to the FMP for the Dolphin and Wahoo Fishery of the Atlantic Hadley and Mehta, 2021

3.2 Spatial distribution

Damiano et al. 2024 Used spatial catch rate data from the US longline observer program to develop spatial indices of abundance using the VAST modeling framework (Thorson 2023). The aggregate (‘total’) index was used to condition spatially aggregated population dynamics models, the seasonal spatial indices were used to impose seasonal-spatial structure for spatial-seasonal operating models.

Similarly to dolphinfish in other oceans (Marín-Enríquez et al. 2018), Damiano et al. found consistent seasonal patterns in spatial distribution and density.

Figure 3. The spatial regions and knots used to define the seasonal - spatial density of dolphinfish.

Figure 4. Seasonal spatial distribution predicted by the VAST model in 2022.

Figure 5. Predicted spatial indices of the base VAST model.

Figure 6. Sensitivity of total (all areas) VAST model estimates to alternative resolution (frequency of knots) of the spatial standardization (panel c is the default configuration).


3.3 Fleet definitions

Table 4. Individual fleets used to partition data for operating model conditioning and projection of fishing dynamics.

Code Fleet Areas Years Selectivity Description
USCom USA commercial longline All All Logistic US pelagic longline fleet
RecN Recreational North NE, NC All Logistic Private recreational fishing
RecS Recreational South SE, SFL All Logistic Private recreational fishing
HireN For hire North NE, NC All Logistic Headboat / party boat / charter
HireS For hire South SE, SFL All Logistic Headboat / party boat / charter
Intl International All All Logistic (USCom) Assumed to have the same seasonal distribution as the USCom fleet.
Disc Discard All All Logistic (USCom) Assumed to have same catches as UnRep fleet and the same seasonal distribution as the USCom fleet.
UnRep Unreported All All Logistic (USCom) Assummed to have the same catches as the Disc fleet and the same seasonal distribution as the USCom fleet.


4 Past data available

Table 5. Summary of data available for operating model conditioning.

Type Resolution Description
Indices Fleet, season, area Derived from a VAST model fitted to U.S. pelagic longline catch and effort data
Catches Fleet, season, area Not available for international and unreported fleets
Effort Fleet, season, area Not available for international and unreported fleets
Length composition Fleet, year Not available for international and unreported fleets


4.1 Catches

Operating models are conditioned on season- and fleet-specific catch data from 1986 to 2022. Identical catches are currently assumed for the Discard and Unreported fleets.

Figure 7. Annual landings by fleet that are assumed in operating model conditioning.


These annual catches are assumed to have the following seasonal distributions where International (Int), Unreported (UnRep) and Discard (Disc) fleets are assumed to have the same seasonality in catches as the US Commercial longline fleet (USCom):


Table 6. Percentage of catches in each season by fishing fleet.

USCom RecN RecS HireN HireS Intl Disc UnRep
Winter 4 0 7 0 8 4 4 4
Spring 54 13 33 29 33 54 54 54
Summer 36 64 48 54 45 36 36 36
Autumn 6 23 11 17 14 6 6 6


4.2 Abundance indices

Spatially aggregated operating models conditioned on the Total abundance index of the VAST spatial model of Damiano et al. 2024

Figure 8. The spatially aggregated (total), seasonal VAST index of Damiano et al. 2024.

The fitted VAST models provide deason estimation of regional biomass for determining seasonal movement matrices.

Figure 9. The spatial, seasonal VAST index of Damiano et al. 2024.

4.3 Length composition of catches

Fishing fleets retain fish of varying sizes. RecS, HireN and HireS fleets have comparable catch length distributions with the majority of fish in the range of 400 - 1000mm with a mode around 550mm (Figure 10) (note that the HireN fleet has relatively few length observations). The USCom fleet catches larger fish from 700 - 1200mm with a mode around 950mm. The RecN fleet catches a wider range of lengths (note that the frequency of observations is relatively low)

Figure 10. Aggregate (across years) length composition data for the catches of those fleets for which these data are available.

4.4 Data assumptions

The key assumptions regarding the data are detailed in Table 7.

Table 7. Assumptions made in the processing, presentation and interpretation of data inputs.

Data type Base assumption (reference case OM)
International annual catches International catches from the FAO database are accurate
Unreported annual catches Unreported annual catches are calculated from the difference between the sum of US commercial, US recreational and international catches and those reconstructed by The Sea Around Us Project. The most recent three years (2020-2022) of unreported caches are based on the mean underreporting from 2010-2019.
Annual discarding The discarding fleet is assumed to have the same catches as the unreported fleet.
Length composition data The lengths are representative of the fish that are landed
Abundance indices The nominal catch and effort data of the US commercial longline fishery are indicative of local scale fish density. Along with spatial and seasonal covariates these are representative of regional and seasonal vulnerable abundance changes over time.
Seasonal catches Seasonality (assignment of annual catches to winter, spring, summer and fall seasons) of the International, Unreported and Discard fleets matches that of the US commercial longline fleet.
Maturity and growth data The data from the regional study of Schwenke and Buckel (2008) are representative of the wider population of the US South Atlantic region.

5 Biological information

5.1 Somatic growth

Growth of individual is assumed to follow a von Bertalannfy growth equation with asymptotic length of 1290 and a maximum growth rate (K) of 0.3175 (per quarter) established by Schwenke and Buckel (2008)

Figure 11. The various regional somatic growth curves for dolphinfish synthezied by Swenke and Buckel (2008) (Figure 3 of that paper). The solid dashed line (‘Present Study’, Florida and North Carolina) is that assumed for the reference case operating model.

5.2 Weight-at-length

MRIP data provides paired observations of weight and length. Fitted exponential curves slightly updated (upward, greater weight at length) the b value estimated by of Schwenke and Buckel (2008) (Figure 12).

Figure 12. MRIP weight at length (b = 0.267, a = 8.2e-8). Schwenke and Buckel (2008) estimated the b value of 0.264 but this did not fit the observed data above. Grey data points were considered outliers and not used in fitting of length-weight curves.

5.3 Maturity-at-age and maturity-at-length

From Schwenke and Buckel (2008): “Males reached 50% maturity at 476 mm FL and 100% maturity was reached at 645 mm FL (Table 3). Females reached 50% maturity at a slightly smaller size than males, although confidence limits for this parameter overlapped with those of males. At 458 mm FL, 50% of female dolphinfish were mature, and 100% were mature at about 560 mm FL”

Figure 13. Maturity (spawning fraction) at age and length for the reference case model, estimated by Schwenke and Buckel (2008)

5.4 Natural mortality rate

Natural mortality rate (M) is highly uncertain for US South Atlantic dolphinfish. Globally, the species has extremely disparate life-history characteristics relating to growth and survival. Fish of the Gulf of Mexico, Caribbean and Mediterranean have much higher growth rate (e.g., K values above 3.0 per year, Molto et al. 2020) and comensurately higher estimated natural mortality rates (e.g., M values higher that 4.0 per year). In contrast, the regional study of Swenke and Buckel estimates much lower growth rates of around 1.27 per year (see section ‘Somatic growth’ above).

Figure 14. Somatic growth summarized by Schwenke and Buckel (2008) versus M estimates presented in Molto et al. 2020.

In the only empirical study directly relevant to the stock of this MSE, the age data collected by Schwenke and Buckel (2008) were from dockside landings in the order of thousands of fish (Buckel personal communication). This would imply relative low natural mortality rates given the relatively large fraction of age 2 and 3 fish that were observed.

Figure 15. The number of fish available dockside to obtain the age 2 and 3+ samples observed by Schwenke and Buckel (2008) for various combinations of fishing mortality (row) and natural mortality (column) per quarter. Note that Buckel indicates (personal communication) that a matter of thousands of fish were available (the blue shaded region). Also shaded in yellow if the region where fishing rates correspond with between 60% and 90% annual harvest rates.

Meta analysis of taxonomically comparable species in the Then et al. (2015) database do not strongly distinguish between varying hypothetical M values but generally support annual values below 2.0.

Figure 16. Meta analysis of growth rate K and natural mortality rate M from the Then et al (2015). dataset for Perciformes and the more closely related Pleuronectiformes.

A preliminary Bayesian analysis of conventional tagging data from a study by Dr Wessley Martin (currently not available for circulation), provided total annual Z (combined natural and fishing mortality rate) estimates of between 1.0 and 2.5 (90% interval) (the range for M is hence lower than these values depending on the assumed fising mortality rate).

Based on these datasets this analysis assumes that M is likely in the range of 0.25 and 0.5 per quarter (1.0 - 2.0 per year). Note that higher M values are likely to be inconsequential to MSE testing as they imply a more productive stock subject to lower current fishing mortality rate (challenges for meeting objectives are generally associated with lower M values).

5.5 Post release mortality rate

The mortality rate of discarded fish is assumed to be relatively high following the study by Rudershausen et al. (2019) with a median value of 0.848 (0.728 - 0.931, 95% CI)

6 Uncertainties

In the workshops of Petersen et al. 2021 stakeholders reported on uncertainties within the dolphin fishery management system that should be incorporated into the MSE exercise.

The following uncertainties were identified by the meeting participants:

6.1 Removals

Participants highlighted the uncertainty in total removals and exploitation rate, in both the magnitude and quality of private recreational catch data and the overall magnitude of international exploitation of the stock.

6.2 Alternate movement patterns

Dolphin movement is generally expected to follow the Gulf Stream from the Caribbean up the US east coast and around the Atlantic Ocean basin back to the Caribbean. Over the past several years, the dolphin population has experienced changes in movement and seasonal residency patterns. Participants suggested that dolphin may be following alternate movement patterns, wherein (1) Caribbean fish are bypassing south Florida as they follow the Gulf Stream, (2) dolphin are moving south from southern New England rather than following the clockwise Atlantic Ocean gyre, and (3) there are populations of resident dolphin in southern New England and off the coast of South Carolina. Participants provided supporting evidence for movement pattern (1), noting that the fish available to North Carolina and surrounding waters match that of dolphin that are caught in Caribbean waters, the clear reduction of dolphin available in south Florida, and the clear increases in water temperature observed off south Florida in recent years.

6.3 Changing availability and catchability

Several mechanisms for past and future changes in availability and catchability were postulated by stakeholders. Some participants suggested that climate-driven changes in temperature, shifts in the location of the Gulf Stream, and declines in the health of Sargassum weed and changes in abundance and clustering of Sargassum weed may lead to changes in availability and catchability across regions. For example, an offshore shift in the Gulf Stream would reduce availability of dolphin to fishermen off the coast of South Carolina, increasing distance of dolphin from shore and forcing fishermen to rely on eddies rather than the main Gulf Stream where the fish may be more abundant, but inaccessible to many fishermen.

Further, anthropogenic changes are expected to impact the availability and catchability of dolphin in the future. In the northern region, fishermen often rely on lobster pot buoys to catch dolphin, which, like fish aggregating devices (FADs), congregate dolphin. However, there is a push to move towards lobster pots with ropeless technology to prevent whale entanglements. This shift would likely impact catchability of dolphin in these affected regions. Contrarily, offshore wind farms are being developed or are planned in several regions along the US east coast. These wind farms will result in increased structure out on the water which will undoubtedly congregate dolphin and their forage. The development of these large offshore wind farms will likely increase the catchability of dolphin in the northern region.

6.4 Economic fishery drivers

The cost of fishing is increasing in all regions, primarily driven by increasing fuel prices. This increasing cost is also exacerbated by dolphin moving further offshore as reported in Wilmington, NC. This strain has certainly been felt across all sectors of the fishery.

Further, the demand for locally caught dolphin varies regionally (is particularly low in the northeast region of the U.S. region) and is highly impacted by imported dolphin. Imported dolphin are primarily Pacific-caught, and imported dolphin account for the vast majority of U.S. dolphin consumption (McPherson et al. 2022). However, stakeholder participants suggested that importing has resulted in a reduction in demand and price for locally caught dolphin across regions. While we can measure and analyze these economic trends from the past, it is much more challenging to predict trends in the cost of fishing and demand for charter trips and locally caught dolphin in the future. Unknown future economic drivers will correspondingly impact future fishing effort.

Post-release mortality and depredation - The at-vessel and post-release mortality reportedly varies substantially by region and is particularly related to areas of high shark abundance. Shark depredation has been increasing over time, particularly in south Florida and northern North Carolina, and this has strong implications for at-vessel and post-release mortality of dolphinfish in these regions.

Enforcement challenges - Participants highlighted challenges with enforcement, particularly related to: unlicensed fishermen selling their product to local restaurants, challenges enforcing stricter state regulations in Florida as compared to federal regulations, and for the implementation of size limits in regions where no size restrictions are currently applied. In particular, in the northern North Carolina region, some feedback included the challenges associated with measuring live dolphin and some indicated that any minimum size limit would be disregarded in practice.

7 Operating model dynamics

7.1 Overview

Operating models for dolphinfish are structured by age, fleet, season and area following the definitions outlined above. This structuring captures important population and fishing dynamics including cohort strength, spatial seasonality in abundance and spatial seasonal fishing opportunities which vary among fishing fleets.

Operating models are conditioned using the Rapid Conditioning Model (RCM, Huynh et al. 2025) of the openMSE R package Hordyk et al. 2025. This is a flexible fishery modelling platform similar to a stock assessment package such as Stock Synthesis or SAM, but designed specifically for the conditioning of operating models in the full range of data-poor to data-rich. A key feature of RCM is the ability to conduct replicate fits across sampled parameters and datasets creating stochasticity in historical dynamics and assumptions. In this configuration for dolphinfish, RCM uses annual catches, the total VAST abundance index (all areas summed) and the fleet composition data to estimate seasonal recruitment, fishing mortality rate at age and numbers at age. The product of an RCM fit is a fully specified operating model for use in openMSE testing of management procedures (MPs). A diagnostic check is available to confirm that the fishing and population dyamics estimated by RCM are exactly matched in the historical reconstruction of the operating model in openMSE that is to be used for testing MPs under projection.

7.2 Population and exploitation dynamics

The operating models assume the standard equations for age-structured population dynamics that include an zero age (season zero in fact) class and a plus group. These along with those for fishery selectivity and exploitation rate are fully documented on the openMSE website. The novel aspect of this operating model is that it is structured by season and so parameters that reflect rates are 1/4 their annual value and recruitment can be estimated to occur in every season.

7.3 Objective function

The equations of the objective functions for priors, data and penalties (e.g., seasonal Fs greater than some specified maximum) are available online from the openMSE website. Operating models were conditioned assuming lognormal likelihoods for catches, the total VAST Index and the length composition data. No ad-hoc reweighting was applied. A maximum seasonal fishing mortality rate (F) of 5.0 was specified above which a penalty was applied.

7.4 Example fit

The RCM model includes a standardized reporting function that describes inputs, model estimates and fit to data. An example of such a report is available here

7.5 Imposition of spatial structure and movement

The seasonal spatial predictions of the VAST model can be used to calculate a constrained movement matrix for every historical season transition (Figure 17). This approach aims for a given level of mean mixing (probability of staying in each area - the positive diagonal) by adjusting a gravity parameter (g) and a viscosity parameter (v) by area.

Figure 17. The estimation of a constrained Markov movement matrix with nareas-1 gravity parameters g, and nareas viscosity parameters (viscosities v, have an informative prior).

This approach is able to well characterize the historical predictions of the VAST model (Figure 18) given varying specification of target viscosity.

Figure 18. Fit of the markov movement matrix to a series of example transitions.

Additionally, these transitions can be sampled from their fit to create historical uncertainty in the movement matrices and also projection uncertainty (Figure 19).

Figure 19. The historical and projected simulation of seasonal-spatial distribution.

This approach to characterizing spatial-seasonal dynamics captures the important properties of the observed abundance changes;

  • Absolute abundance differences among areas
  • Seasonality in abundance
  • Varying seasonality among areas
  • Common overall trends in abundance

7.6 Management control options

In this preliminary TSD, possible management control options have yet to be clearly identified for the purposes of management procedure testing. These could (but may not) include catch limits, effort limits, minimum size limits, slot limits, time-area closures, alternative gear configurations, discard mortality reduction devices and bag/trip limits for individual species or groups of species.

For now these options remain up for discussion and some may simply be reserved for exploration or sensitivity testing. More information on current management options is included in Amendment 10 to the Fishery Management Plan for the Dolphin and Wahoo Fishery of the Atlantic

7.7 Generation of Future Data for Input to Management Procedures

When an RCM model is fitted to catches, indices and length composition data, the properties of the observation error model (that generates future data of comparable quality to those observed historically) is based on the fit of each simulation to the observed data. For example, if a simulation fits the data well (has low residual error and few runs in residuals) then future observations of those data will be generated with comensurately high precision and low lag-1 autocorrelation (that determines whether data are consistently over or under the true simualted value). Conversely, if a simulation (a fit of the model to the data) does not fit the data well, then those data are simulated with higher precision and, if there are runs in residuals, higher lag-1 autocorrelation. For composition data, the effective sample size of historical data is assumed for future simulations.

7.8 Implementation of management advice

In the reference case operating model, management advice is considered to be followed exactly for all measures including size limits, effort controls and catch limits (essnetially identical to those specified by management). Mismatches between recommendations and the implemented exploitation can still occur if the simulated stock reaches low levels and catches are not obtainable, for example.

8 Trial Specifications

8.1 Reference Set operating models

Reference set operating model represent the primary uncertainties relating to stock, fishery, observation and implementation dynamics. In most MSE settings, typical more uncertainties are similar to common sensitivity analyses for stock assessment models including:

  • stock status (e.g., spawning biomass relative to unfished levels)
  • productivity (e.g., mean recruitment level, natural mortality rate, somatic growth)
  • resilience (i.e., steepness or compensation ratio)
  • magnitude of historical observed catches
  • weighting of conflicting stock trend information

Reference set operating models serve as the core basis for comparatively evaluating the performance of candidate management procedures (CMPs). These reference set operating models may be weighted to reflect varying plausibility of the simulated scenarios. In general, refernece set operating models may be empirically derived and are not specified from only expert judgement or other subjective approaches.

For dolphinfish, spatial considerations of stock distribution, stock mixing and fishing opportunity are additional key uncertainties. Preliminary reference set operating models were designed as an orthogonal grid of 5 factors with two levels of each factor. Those factors included natural survival (M), magnitude in mean recruitment (based on time periods of historical estimated recruitment), stock resilience (steepness), spatial distribution and stock viscosity. With two levels for each factor this leads to a total of 32 operating models (Table 8. Uncertainty in stocks status is included within operating models of the refernece grid and characterized by stochasticity among the RCM conditioned simulations in each operating model.

Table 8. The factors and levels of the orthogonal grid of reference set operating models.

Uncertainty Level 1 Level 2
Natural Mortality Low (m): 1.0 per year High (M): 2.0 per year
Recruitment Level Low (r): as last 10 years High (R): all years
Resilience (steepness) Low (s): 0.7 High (S): 0.95
Spatial distribution US obs (d) With expert judgement (D)
Viscosity Low (v): prob. stay. = 0.6 High(V) prob. stay. = 0.9


8.2 Reference Case operating model

The reference case operating model is a single well-characterized and well-understood hypothesis that serves as a familiar and concise basis for quickly investigating sensitivities, alternative MPs etc. It is a tool for efficiently obtaining quantitative answers to working group questions (e.g., “does it make a difference to MP selection if historical catches are under reported”?).

At this early stage no reference case operating model has been selected.

8.3 Plausibility weighting

Currently, reference set operating models are considered as equally plausible. Future phases of MSE development may include processes to add weighting of operating models.

8.4 Robustness set operating models

Robustness set operating models represent secondary uncertainties that may not be well informed by data, involve alternative future scenarios that are derived from subjective judgement or represent the viewpoints or experience of particular stakeholders or scientists. The purpose of the robustness operating models is to provide a means of further discrimation among management procedures that perform similarly for the reference set of operating models. Robustness operating models also provide performance outcomes for hypotheses specific to certain stakeholder groups (e.g., “how will northward shifts in biomass affect catch outcomes for Florida fisheries”?).

Table 9. Robustness operating models. Single factor variants of the reference case operating model.

Code Description
C1 Catch reconstruction consistent with the SAUP estimates
C2 Seasonal catch distribution of international, discard and unreported fleets matches the Rec and Hire fleets.
C3 IUU increases by 1% every year
R1 Future recruitment declines 1% per year
R2 Future recruitment reduces by 25% after 5 years
R3 Future recruitment reduces by 25% after 10 years
R4 Future recruitment is 50% more variable
S1 Two percent decline in CAR and SFL, 1 percent increase in SE, 2% increase in NC and NE
S2 50% greater variability in spatial / seasonal distribution
S3 1% pa. increase in catchability reflecting range contraction
P1 1% pa. decrease in somatic growth rate (k)
P2 1% pa. decrease in condition factor (weight at length)
P3 1% pa. increase in natural mortality rate (all ages)

8.5 Sensitivity operating models

In the development of an MSE framework hypotheses for operating models may not be consequential to the selection of MPs but provide useful secondary information for guiding management, for example the impact of more reliable indices, better catch monitoring, more frequent management updates etc. Sensitity operating models can characterize these hypotheses, they also provide a place to store hypothesis that were considered for reference or robustness sets but were deemed too similar to other hypotheses or were inconsequential.


9 Climate robustness and ecosystem considerations

9.1 Introduction

In most fishery management settings there is an impetus to demonstrate that adopted management procedures are responsive and robust to plausible climate impacts.

A review of papers that describe possible climate impacts on fisheries revealed very few examples where defensible forecasts were available (Carruthers 2024a). In theory, it may be possible to develop an ‘end-to-end’ model that combines sub-models of emissions (e.g., Algieri et al. 2023, Wang et al. 2017), earth systems (Kawamiya et al. 2020), ecosystems (e.g., Beaugrand and Kirby 2018, Lehodey et al. 2010; 2011), behaviour (e.g., Bushnell and Brill 1991, Cayré and Marsac 1993) and physiology (e.g., Gooding et al. 1981, Graham et al. 1989, Checkley et al. 2009). Forecasting fishery impacts would therefore combine a complex series of linked projections that include greenhouse gas emissions (least uncertain), response of climate processes (uncertain), linkages with oceanographic conditions (more uncertain) and the expected impact of those on pelagic communities and individual species (most uncertain). It can be argued that any forecast of climate impacts on fisheries should be seen as firmly hypothetical, and the relative credibility of impact scenarios should be considered highly uncertain.

This large uncertainty over climate impact scenarios poses a problem for the provision of ‘climate ready’ fishery management advice using the contemporary stock assessment and management strategy evaluation (MSE) frameworks. That is because those frameworks rely on the specification of models that represent climate impacts and the frequency (weighting) of those models could strongly affect the advice provided. For example, it may not be clear whether there will be small or large future changes in natural survival (natural mortality, M). Advice arising from scenarios with large M changes would likely lead to the provision of strongly differing advice from scenarios with small M changes, yet their relative credibility is not easily evaluated.

9.2 A proposal using MSE simulations

Although quantitative forecasts of climate impacts on fisheries may not be available, qualitatively, the way in which climate can impact individual populations is clear. Most papers documenting possible impacts predict changes in recruitment strength (carrying capacity, spawning habitat, larval survival), adult survival (natural mortality rate), somatic growth, spatial distribution (range contraction, catchability) age at maturity and condition factor (fecundity). Additionally, it is generally understood what direction of change in those variables poses a challenge for management procedures: lower recruitment strength, decreased survival, lower somatic growth rate, reduced spatial distribution, older age at maturity and poorer condition factor.

One proposed option (Carruthers 2024b) is to shift the focus from model-based tests of climate robustness in favour of performance metrics of climate robustness. Such an approach tests how resilient candidate management procedures are to commonly proposed climate impacts such that their climate robustness can be comparatively evaluated. This shifts the emphasis away from forecasting (we think this climate impact will happen) to the inherent robustness of the candidate management procedures (if this impact occurs, this MP is twice as resilient).

9.3 Calculating climate robustness metrics

It is possible to quantify climate robustness with respect to declines in population survival (natural mortality rate M), recruitment strength (R), somatic growth (K) and condition factor (C). To do this MPs are tuned to acheive stable biomass outcomes and then the level of change required to break a threshold is calculated. This then becomes a performance attribute of that management procedure.

Figure 20. Calculation of climate robustness metrics. In this example an MP is tuned such that it provides stable SSB over 20 projected years. Given a defined threshold for ‘robust’ as a decline in biomass of more than 30% after 20 projected years, it is possible to calculate the decrease in somatic growth (K) needed to ‘break’ the MP such that it drops below the threshold. In this case the index target MP allowing for 10% changes in TAC between years (bottom) was more than twice as robust as the derivative allowing for 5% changes (top) (surviving up to an 18% decline in K vs an 8% decline, respectively).

These types of metrics can then be tabulated across MPs (rows) and climate tests (column):

Table 10. Example climate performance metrics. Tabulated numbers are the percentage change in each impact before the robustness threshold is reached. Higher percentages that are shaded green represent higher robustness. Shading is scaled per climate test (by column) according to the maximum robustness (highest %) M = increasing natural mortality rate, R = decreasing recruitment strength, K = decreasing somatic growth, C = decreasing condition factor (weight-at-age).

10 Performance measures / statistics

In this preliminary TSD, straw-dog perfomrance measure and metrics are described only for the purposes of gathering feedback.

10.1 Performance measures

There typically three principal axes for evaluating CMP performance over the short, medium and long terms:

  • Yield
  • Stability in Yield
  • Resource conservation

Other axes can include:

  • Catch rates (e.g., fish per trip, fraction of successful trips)
  • Effort (e.g., No. trips)
  • Size of fish caught

10.2 Performance metrics

In each case a performance measure can be represented by a reproductive quantitative metric. For example, short-term yield could be the mean expected yield from 2025-2034 (e.g., 11).

Table 11. Example, straw dog performance measures and metrics.

Measure Metric Term Years
Yield Mean Yield All 2025 - 2054
“” “” Short 2025 - 2034
“” “” Medium 2035 - 2044
“” “” Long 2045 - 2055
Stability in yield Mean % change in yield All 2025 - 2054
Biomass conservation Prob. SSB < lowest historical All 2025 - 2054
“” “” Short 2025 - 2034
“” “” Medium 2035 - 2044
“” “” Long 2045 - 2055

10.3 Performance summary statistics

In principle it is possible to score across metrics by combining and potentially weighting these. If summary statistics are developed this generally happens nearer to the end of the MP selection process.

10.4 Results presentation

The default basis for the presentation of results is the Slick App Hordyk et al. 2025 that provides an interactive summary of MSE results across OMs, MPs and performance metrics. Results are presented in worm plots, spider plots, Zeh (bar) plots and kobe plots in addition to color-coded performance tables sometimes referred to as ‘quilt plots’.


11 Management Procedures

11.1 Management procedures archetypes

MP archetypes are broad classifications of management procedures that interpret data types in a particular way for the provision of management advice. For example an index rate output MP is one that aims for a constant exploitation rate.

Table 12. Examples of candidate management procedure archetypes.

Archetype Description
Index rate output Catch limits are calculated as a constant fraction of the observed index (constant harvest rate)
Index rate input Effort, size limits or bag limits are adjusted to obtain a target rate of catch per index level
Index target Catches, effort, size limits or bag limits are adjusted to achieve a target index level
Index slope Catch limits, effort, size limits or bag limits are adjusted to obtain a particular schedule of index slopes (e.g. rebuild then stable)

11.2 Management procedure derivatives

A management procedure archetype can be modified into a derivative by imposing one or more constraints, filters or harvest control rules.

Table 13. Examples of candidate management procedure derivatives

Derivative Description
Constrained management change Changes in catch limits, effort etc are constrained between minimum and maximum bounds between management cycles
Damped management change Changes in catch limits, effort etc are reduced or exaggerated
Maximum extent of management E.g. a maximum cap on catch limits, effort limits, size limits etc
Alternative management cycle Updated management advice over a larger or smaller number of time steps
Alternative data lag Management advice calculated from data up to a given year prior to advice year
Data weighting Alternative emphasis on data streams entering the management procedure (e.g. indices from various areas)
Data smoothing Alternative filtering method or strength to reduce noise in input data / increase MP stability
Harvest control rule Imposition of a hockey-stick, or similar harvest control rule that throttles exploitation below a particular index level

11.3 Management procedure tuning


12 Exceptional Circumstances Protocols

12.1 The Role of Exceptional Circumstances

A principal motivation behind management strategy evaluation (MSE) and the management procedure (MP) approach was originally to lessen the need for frequent use of more complex and comprehensive stock assessment processes (and associated ‘tinkering’, Butterworth 2008). It follows that in most settings, MPs are adopted for an agreed period of time after which a formal review of the MP is scheduled. For example, the 5-year interval for review of MP implementation by the International Whaling Commission, IWC 1999 and the 6-year interval for review of the MP adopted by ICCAT for Atlantic bluefin tuna (ICCAT2022).

It is however considered best practice to establish protocols for detecting situations where the observed system dynamics are not consistent with the range of simulations specified in the operating model, over which the adopted MP was demonstrated to be robust (Butterworth 2008). Exceptional circumstances (EC) protocols typically compare new, updated observations of the data used by the MP with the simulated values from the MSE projections (‘posterior predicted data’). They can also involve a check of the assumptions used to condition the models or characterize the axes of uncertainty.

12.2 An example: Northern Atlantic Albacore Exceptional Circumstances

ICCAT recently adopted an EC protocol for North Atlantic albacore (ICCAT 2021a; b). The EC protocol identifies several indicators which may trigger exceptional circumstances if new observed values fell outside the 95% interquantile range of the simulated values for a given year. Other indicators such as growth, maturity, and natural mortality were highlighted as possible triggers of ECs but their criterion for being triggered was less exact, simply indicating when these should be considered (e.g., new research/analysis was presented and accepted by the SCRS leading to new values substantially different from those used in the MSE testing).

The northern albacore EC is not rigidly connected with a specified action: “Triggering an EC does not immediately result in TAC advice from the MP being rescinded; rather, it means that the SCRS needs to examine the indicators and determine if a change in advice is warranted.”

The triggering of an exceptional circumstance first requires a determination of how the EC might impact the results of the management procedure currently being used. Triggering EC could be interpreted in several ways, for example: (1) there is conservation concern as indicators/inputs are lower than those tested; (2) yields are potentially too low as indicators/inputs are higher than those tested, (3) there is no appreciable change in conservation concern or potential yield as the EC has little impact on the MP results. Once the impact of the EC has been established the recommended responses to the EC can be developed for consideration by managers.

12.3 Other Examples of Exceptional Circumstances

In the case of Greenland halibut in Subarea 2 + Divisions 3KLMNO, the Northwest Atlantic Fisheries Organization (NAFO 2011) compares observed catches and survey indices with operating model predictions of these data. When observations fall out of the 90% probability interval of posterior predicted data, these are considered indicative of exceptional circumstances. Exceptional circumstances provisions were triggered for Greenland halibut (NAFO 2018) in 2014 when one of the survey index observations fell below the 5th percentile of the predicted posterior distribution for that data type.

Similarly to NAFO, the Canadian Department of Fisheries and Oceans (DFO 2011) identify various data that may be monitored to detect exceptional circumstances in the MSE for Pollock in NAFO area 4Xopqrs5, including survey biomass indices, catches and catch-at-age composition data. DFO (2011) also identified being outside the 90% probability interval of simulated survey data as a possible indicator of ECs and included an absolute level for the posterior predicted indices below which observed data would indicate exceptional circumstances.

For southern bluefin tuna, the EC protocols of CCSBT make use of fishery indicators (relative abundance data not used by the MP) and a range of data types used by the MP including fishery-dependent catch rate indices, estimates of spawning biomass from close-kin genetics and estimates of age-2 numbers from conventional gene tagging (Preece et al. 2021). Similarly to northern albacore, CCSBT outline exceptional circumstances in order to flag possible problem areas to keep under closer scrutiny for the immediate future, rather than rigidly connecting EC to a course of action such as operating model reconditioning and/or MP revision. For example, a very high Japanese longline CPUE estimate for 2018 triggered the CCSBT EC but action was not taken given that there was a relatively low impact on the TAC recommendation (Preece et al. 2021). Similarly to EC in other MSE frameworks, the focus of the EC is not the ability to detect a specified OM condition (e.g., low spawning biomass) but on whether observed data are comparable to those projected by the OM.

The Indian Ocean Tropical Tuna Commission (IOTC) also has a broad definition of exceptional circumstances (IOTC 2021) including: “new knowledge about the stock, population dynamics or biology, changes in fisheries or fishing operations, changes to input data to the MP, or missing data, or inconsistent implementation of the MP advice (e.g. total catch is greater than the Total Allowable Catch)”. Similarly to CCSBT, IOTC does not prescribe a rigid management response to the triggering of EC, and this “can include review of additional information or new research, review of the performance of the MP (via reconditioned Operating Models), or management advice to precautionarily revise the TAC”.

12.4 ECP development for dolphinfish

ECP are the final component of MSE framework development and are typically explored during the MP adoption phase.


13 References

Aires-da-silva, A., J. L. Valero, M. N. Maunder, C. Minte-Vera, C. Lennert-Cody, M. H.Román, J. Martínez-Ortiz, E. J. Torrejón-Magallanes, and M. N. Carranza.Exploratory Stock Assessment of Dorado (Coryphaena hippurus) in theSoutheastern Pacific Ocean. Document SAC-05-11b. Inter-American Tropical TunaCommission, Scientific Advisory Committee, Seventh Meeting. pp. 9–13 (2016).

Aires-da-Silva, A., C. E. Lennert-Cody, M. N. Maunder, M. Roman-Verdesoto, C. Minte-Vera, N. W. Vogel, J. Martínez-Ortiz, J. C. Carvajal, P. X. Guerrero, and F.Sondheimer. Preliminary Results from IATTC Collaborative Research Activities onDorado in the Eastern Pacific Ocean and Future Research Plan. Document SAC-05-11b. Inter-American Tropical Tuna Commission, Scientific Advisory Committee,Fifth Meeting. pp. 1–27 (2014).

Alejo-Plata, C., P. Díaz-Jaimes, and I. H. Salgado-Ugarte. Sex ratios, size at sexual maturity, and spawning seasonality of dolphinfish (Coryphaena hippurus) captured in the Gulf of Tehuantepec, Mexico. Fish. Res., 110(1): 207–216 (2011b).

Alemany, F., and E. Massuti. First record of larval stages of Coryphaena hippurus(Pisces: Coryphaenidae) in the Mediterranean Sea*. Sci. Mar., 62(1–2): 181–184 (1998).

Arocha, F., L. A. Marcano, A. Lárez, D. Altuve, and J. Alió. The fishery, demographic size structure and oocyte development of dolphinfish, Coryphaena hippurus, in Venezuela and adjacent waters*. Sci. Mar., 63(3–4): 401–409 (1999).

Beardsley, G. L. (1967). Age, growth, and reproduction of the dolphin, Coryphaena hippurus, in the Straits of Florida. Copeia 1967, 441–451. doi: 10.2307/1442132

Benjamin, D., and B. Kurup. Stock assessment of Dolphinfish, Coryphaena hippurus (Linnaeus, 1758) off southwest coast of India. J. Mar. Biol. Assoc. India, 54(1): 96 (2012).

Bentivoglio, A. A. Invesigations into the growth, maturity, mortality rates and occurrenceof the dolphin (Coryphaena hippurus, Linnaeus) in the Gulf of Mexico. M.Sc. thesis. University College of North Wales, Bangor, UK, 37 p. (1988).

Bignami S, Sponaugle S, Cowen RK. Effects of ocean acidification on the larvae of a high-value pelagic fisheries species, Mahimahi Coryphaena hippurus. Aquat Biol. 2014;21:249-260. https://doi.org/10.3354/ab00598.

Brewton, R. A., M. J. Ajemian, P. C. Young, and G. W. Stunz. Feeding Ecology of Dolphinfish in the Western Gulf of Mexico. Trans. Am. Fish. Soc., 145(4): 839–853 (2016).

Brill R. Selective advantages conferred by the high performance physiology of tunas, billfishes, and dolphin fish. Comp Biochem Physiol. 1996;113A:3-15.

Carruthers, T.R. 2024a. Developing the Climate Test: Robustness Trials for Climate-Ready Management Procedures. Collect. Vol. Sci. Pap. ICCAT, 81(6), SCRS/2024/104: 1-26 (2024). Available from: https://www.iccat.int/Documents/CVSP/CV081_2024/n_6/CV08106104.pdf

Carruthers, T.R. 2024b. Developing the Climate Test: Performance metrics of climate robustness. Collect. Vol. Sci. Pap. ICCAT, 81(2), SCRS/2024/148: 1-7 (2024). Available from https://www.iccat.int/Documents/CVSP/CV081_2024/n_2/CV08102148.pdf

Catalán, I. A., D. Auch, P. Kamermans, B. Morales-Nin, N. V. Angelopoulos, P. Reglero, T. Sandersfeld, and M. A. Peck. Critically examining the knowledge base required to mechanistically project climate impacts: A case study of Europe’s fish and shellfish. Fish Fish., 1–17 (2019).

Chang, S.-K., and M. N. Maunder. Aging material matters in the estimation of von Bertalanffy growth parameters for dolphinfish (Coryphaena hippurus). Fish. Res., 119–120: 147–153 (2012).

Chatterji, A., and Z. A. Ansari. Fecundity of dolphin fish, Coryphaena hippurus L. Mahasagar-Bulletin Natl. Inst. Oceanogr., 15(2): 129–133 (1982).

Ditty, J. G., R. F. Shaw, C. B. Grimes, and J. S. Cope. Larval development, distribution, and abundance common dolphin, Coryphaena hippurus, and pompano dolphin, C. equiselis (family: Coryphaenidae), in the northern Gulf of Mexico*. Fish. Bull., 92(2): 275–291 (1994).

Dooley, J. K. Fishes associated with the pelagic Sargassum complex, with a discussion of the Sargassum community. Contrib. Mar. Sci., 16: 32 (1972).

FAO, Coryphaena hippurus (Linnaeus, 1758). Global Capture Production for Species. 2018. http://www.fao.org.

Farrell, E. R., A. M. Boustany, P. N. Halpin, and D. L. Hammond. Dolphinfish (Coryphaena hippurus) distribution in relation to biophysical ocean conditions in the northwest Atlantic. Fish. Res., 151: 177–190 (2014).

Guzman, H. M., E. Díaz-Ferguson, Á. J. Vega, and Y. A. Robles. Assessment of the dolphinfish Coryphaena hippurus (Perciformes: Coryphaenidae) fishery in Pacific Panama. Rev. Biol. Trop., 63(3): 705–716 (2015).

Hadley, J., Mehta, K.N. 2021. Amendment 10 to the Fishery Management Plan for the Dolphin and Wahoo Fishery of the Atlantic. Available from https://repository.library.noaa.gov/view/noaa/32989

Heuer, R. M., Stieglitz, J. D., Pasparakis, C., Enochs, I. C., Benetti, D. D., Grosell, M. (2021). The effects of temperature acclimation on swimming performance in the pelagic Mahi-mahi (Coryphaena hippurus). arXivLabs [Preprint]. Available online at: https://arxiv.org/abs/2102.07743 (accessed February 15, 2021)

Hordyk, A. 2025. Slick decision analysis. Available from: https://harveststrategies.org/slick-user-guide.html

Hordyk, A., Huynh, Q., Carruthers, T. 2025. OpenMSE: An open-source R package for Management strategy evaluation, available from: https://openmse.com

Huynn, Q., 2025. Rapid Conditioning model. Available from https://openmse.com/tutorial-rcm/

Kitchens, L. L., and J. R. Rooker. Habitat associations of dolphinfish larvae in the Gulf of Mexico. Fish. Oceanogr., 23(6): 460–471 (2014).

Lin, S.-J., Musyl, M. K., Wang, S.-P., Su, N.-J., Chiang, W.-C., Lu, C.-P., et al. (2019). Movement behaviour of released wild and farm-raised dolphinfish Coryphaena hippurus tracked by pop-up satellite archival tags. Fish. Sci. 85, 779–790. doi: 10.1007/s12562-019-01334-y

Marín-Enríquez, E., J. Seoane, and A. Muhlia-Melo. Environmental modeling of occurrence of dolphinfish (Coryphaena spp.) in the Pacific Ocean off Mexico reveals seasonality in abundance, hot spots and migration patterns. Fish. Oceanogr., 27(1): 28–40 (2018).

McGuigan, C. J., Schlenker, L. S., Stieglitz, J. D., Benetti, D. D., and Grosell, M. (2021). Quantifying the effects of pop-up satellite archival tags on the swimming performance and behavior of young-adult mahi-mahi (Coryphaena hippurus). Can. J. Fish. Aquat. Sci. 78, 32–39. doi: 10.1139/cjfas-2020-0030

Merten, W., R. Appeldoorn, and D. Hammond. Movements of dolphinfish (Coryphaena hippurus) along the U.S. east coast as determined through mark and recapture data.Fish. Res., 151: 114–121 (2014a).

Merten, W., R. Appeldoorn, and D. Hammond. Spatial differentiation of dolphinfish(Coryphaena hippurus) movements relative to the Bahamian archipielago. Bull. Mar. Sci., 90(3): 849–864 (2014b).

Merten, W., Appeldoorn, R., and Hammond, D. (2016). Movement dynamics of dolphinfish (Coryphaena hippurus) in the northeastern Caribbean Sea: evidence of seasonal re-entry into domestic and international fisheries throughout the western central Atlantic. Fish. Res. 175, 24–34. doi: 10.1016/j.fishres.2015.10.021

Merten, W., Appeldoorn, R., Rivera, R., and Hammond, D. (2014). Diel vertical movements of adult male dolphinfish (Coryphaena hippurus) in the western central Atlantic as determined by use of pop-up satellite archival transmitters. Mar. Biol. 161, 1823–1834. doi: 10.1007/s00227-014-2464-0

Molto V. et al. 2020. A global review on the biology of the dolphinfish (Coryphaena hippurus) and its fishery in the Medterranean Sea: advances in the last two decades. Reviews in Fisheries Science and Aquaculure. 28 (3): 376-420. Available fromhttps://digital.csic.es/bitstream/10261/225596/4/Molto_etal_2020.pdf

Oxenford, H. A. (1999). Biology of the dolphinfish (Coryphaena hippurus) in the western central Atlantic: a review. Sci. Mar. 63, 277–301. doi: 10.3989/scimar. 1999.63n3-4303

Oxenford, H. A., and Hunte, W. (1999). Feeding habits of the dolphinfish (Coryphaena hippurus) in the eastern Caribbean. Sci. Mar. 63, 303–315. doi: 10.3989/scimar.1999.63n3-4317

Palko BJ, Beardsley GL, Richards WJ. Synopsis of the biological data on dolphin-fishes, Coryphaena hippurus Linnaeus and Coryphaena equiselis Linnaeus. FAO Fish Synopsis Rep. 1982;130: 1-34

Perry, A. L., Low, P. J., Ellis, J. R., and Reynolds, J. D. (2005). Climate change and distribution shifts in marine fishes. Science 308, 1912–1915. doi: 10.1126/science.1111322

Peterson, C., Karnauskas, M., McPherson, M., Hadley, J., Blake, S., Byrd, J. 2024. Report of the South Atlantic dolphinfish management strategy evaluation stakeholder workshops. NOAA Tech. Memo. NMFS-SEFSC-781, 35 p. Available from https://repository.library.noaa.gov/view/noaa/61285

Pimentel M, Pegado M, Repolho T, Rosa R. Impact of ocean acidification in the metabolism and swimming behavior of the dolphinfish (Coryphaena hippurus) early larvae. Mar Biol. 2014;161:725-729. https://doi.org/10.1007/s00227-013-2365-7.

Punt, A.E., Butterworth, D.S., de Moor, C.L., De Oliveira, J.A.A., and Haddon, M. 2016. Management strategy evaluation: Best practices. Fish Fish. 17(2): 303–334. doi:10.1111/faf.12104.

R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.r-projecto.org. (2024).

Rose, C. D. The biology and catch distribution of the dolphin, Coryphaena hippurus(Linnaeus), in North Carolina waters. PhD Thesis. North Carolina State University(1966).Rose, C. D., and W. W. Hassler. Age and Growth of the Dolphin, Coryphaena hippurus (Linnaeus), in North Carolina Waters. Trans. Am. Fish. Soc., 97(3): 271–276 (1968).

Rose, C. D., and W. W. Hassler. Food Habits and Sex Ratios of Dolphin Coryphaena hippurus Captured in the Western Atlantic Ocean off Hatteras, North Carolina.Trans. Am. Fish. Soc., 103(1): 94–100 (1974).

Rudershausen, P.J., Poland, S.J., Merten, W., Buckel, J.A., 2019. Estimating Discard Mortality for Dolphinfish in a Recreational Hook and Line Fishery. N. Am. J. Fish. Manage. 39(6): 1143-1154.

Salvadeo, C., Auliz-Ortiz, D. M., Petatán-Ramírez, D., Reyes-Bonilla, H., IvanovaBonchera, A., and Juárez-León, E. (2020). Potential poleward distribution shift of dolphinfish (Coryphaena hippurus) along the southern California current system. Environ. Biol. Fishes. 103, 973–984. doi: 10.1007/s10641-020-00999-0

Schwenke, K.L., and Buckel, J.A. 2008. Age, growth, and reproduction of dolphinfish (Coryphaena hippurus) caught off the coast of North Carolina. Fish. Bull. 106:82–92 (2008)

Stieglitz, J. D., Hoenig, R. H., Kloeblen, S., Tudela, C. E., Grosell, M., and Benetti, D. D. (2017). Capture, transport, prophylaxis, acclimation, and continuous spawning of mahi-mahi (Coryphaena hippurus) in captivity. Aquaculture 479, 1–6. doi: 10.1016/j.aquaculture.2017.05.006

Stieglitz JD, Benetti DD, Grosell M. Nutritional physiology of mahi-mahi (Coryphaena hippurus): Postprandial metabolic response to different diets and metabolic impacts on swim performance. Comp Biochem Physiol A Mol Integr Physiol. 2018;215: 28-34. https://doi.org/10.1016/j.cbpa.2017.10.016.

Stramma, L., Prince, E. D., Schmidtko, S., Luo, J., Hoolihan, J. P., and Visbeck, M. (2012). Expansion of oxygen minimum zones may reduce available habitat for tropical pelagic fishes. Nat. Clim. Chang. 2, 33–37. doi: 10.1038/nclimate1304

Then, A.Y., Hoenig, J.M., Hall, N.G., Hewitt, D.A. 2015. Evaluating the predictive performance of empirical estimators of natural mortality rate using information on over 200 fish species. ICES Journal of Marine Science. 72(1): 82–92, https://doi.org/10.1093/icesjms/fsu136

Thorson, J.T., 2019. Guidance for decisions using the Vector Autoregressive Spatio-Temporal (VAST) package in stock, ecosystem, habitat and climate assessments. Fish. Res. 210, 143–161. https://doi.org/10.1016/j.fishres.2018.10.013

Tsai HY, Chang M, Liu SC, Abe G, Ota KG. Embryonic development of goldfish (Carassius auratus): A model for the study of evolutionary change in developmental mechanisms by artificial selection. Dev Dyn. 2013;242:1262-1283. https://doi.org/10.1002/dvdy.24022.

14 Appendix A. Glossary

Table 14. Glossary of MSE terminology and acronyms

Term Description
MSE Management Strategy Evaluation: a participatory process to establish management procedures (harvest strategies) that are robust to uncertainties in fishery and population dynamics.
OM Operating Model: a mathematical description of fishery and population dynamics codified in a simulation framework for the robustness testing of candidate management procedures.
MP Management Procedure (harvest strategy): a algorithm that calculates management advice from data (real or simulated).
CMP Candidate Management Procedure. One of multiple possible management procedures that is to be comparatively evaluated by MSE.
MSE framework The process, membership, meetings, documents, software package, management objectives and exceptional circumstances protocols that support the adoption of a management procedures.
Closed-loop simulation The engine at the heart of MSE simulations: a codified representation of fishery and population dynamics (operating model) linked to an observation error model (data generation) a candidate management procedure, an implementation model (controls adherence to management advice) which accounts for feedback between the fishery system, data, recommendations and management actions to quantify management performance.
TSD Trial Specifications Document: a description of the methodology of the MSE framework that ensures reproducibility including all decisions, background information and equations.
Reference Case A single operating model familiar to the working group that can be used for didactive purposes such as exploring ideas, demonstrating concepts / sensitivities.
Reference Set A set of operating models, sometimes represented by an orthogonal grid of operating models that represent the core uncertainties that CMPs should be robust to: the primary basis for the evaluation of CMPs.
Robustness Set A secondary set of operating models used to further distinguish between CMPs that otherwise perform similarly for the reference set of OMs. These may include hypotheses that have a relatively weak empirical basis or uncertain future conditions for projections.
Data guillotine A date after which new data will not be accepted for use in operating model or management procedure development.
OM conditioning The process of fitting operating models to observed data statistically (similar to fitting of stock assessment models).
EC Protocols Exceptional Circumstances protocols: an empirical check that observed data are consistent with those data expected to be observed when the MP is in use (a basis for detecting departures in systems dynamics away from the operating models for which the MP was demonstrated to be robust).